Author: catavera

As discussed in our team status report, we have made many changes to the scope and goals of our project based on the feedback we received after presenting our proposal. Most notably, we will no longer be detecting pitch or rhythm in real-time, nor will we be evaluating a singer’s performance with respect to that of an uploaded song, both of which were aspects of the project that I was responsible for. We will not be implementing pitch detection in real-time because of unrealistic latency bounds. Now, pitch and rhythm detection and feedback will be provided after a performance. This makes pitch detection significantly easier because there already exist several well-researched pitch detection algorithms (PDAs). I will be implementing our pitch detector using the autocorrelation method, which excels in estimating monophonic pitch. I plan on implementing this pitch detector by the end of this week.

Given that our app will no longer provide real-time feedback, we decided that it would be nice to include more features that are indicators of good singing. One such feature is the phonetogram which measures a singer’s singing intensity at a given frequency, thus a good indicator of a singer’s range.

I have also very recently come across a wholistic singing quality metric called the Perceptual Evaluation of Singing Quality (PESnQ) score as described here. I see great promise in this metric for our purposes and will read the paper in more detail. With this metric, I think we have enough to provide users’ with sufficient feedback on their performance.

This week I’ve been researching additional vocal features for our system that work well as discriminators for good and bad singing. The best metric that I’ve seen so far is called the Singing Power Ratio (SPR). Next week, I will start implementing the real-time pitch and timing detection systems.